[Paper] FluxSieve: Unifying Streaming and Analytical Data Planes for Scalable Cloud Observability

Source: arXiv - 2603.04937v1

Overview

Modern observability platforms must ingest massive streams of logs, metrics, and traces while still supporting ad‑hoc analytical queries. The paper “FluxSieve: Unifying Streaming and Analytical Data Planes for Scalable Cloud Observability” proposes a new architecture that pushes lightweight filtering and enrichment directly into the ingestion pipeline, effectively merging the traditional pull‑based query engine with push‑based stream processing. By doing so, it slashes the cost of repetitive, compute‑heavy filters that would otherwise run at query time.

Key Contributions

• Unified data‑plane design – Introduces a single ingestion‑time layer that performs pre‑computation and filtering, eliminating the need for a separate stream‑processing framework.
• Scalable multi‑pattern matching – Implements a concurrent, on‑the‑fly updatable rule engine that evaluates many filter predicates per record with sub‑microsecond overhead.
• Integration with existing OLAP stacks – Demonstrates seamless coupling with Apache Pinot (real‑time OLAP) and DuckDB (embedded analytical DB), showing that the approach works with both distributed and single‑node analytics.
• Comprehensive evaluation – Shows up to 2–3 orders of magnitude faster query latency for common observability workloads, while adding only a few percent extra storage and negligible CPU cost during ingestion.

Methodology

1. Ingestion‑time pre‑filtering – As each telemetry record arrives, FluxSieve runs a compact rule engine that matches the record against a set of user‑defined filter patterns (e.g., regexes, field predicates). Matching records are tagged or dropped, and optional enrichment fields are materialized.
2. Rule engine design – The authors built a deterministic finite automaton (DFA)‑based multi‑pattern matcher that can be updated without stopping the pipeline. Rules are stored in a shared, lock‑free structure, allowing concurrent reads while new patterns are added or retired.
3. Data‑plane coupling – The filtered/enriched stream is then handed off to the downstream analytical store (Pinot or DuckDB). Because the heavy filtering work is already done, the analytical engine only needs to scan a much smaller, already‑indexed dataset.
4. Experimental setup – They replayed real‑world observability traces (≈ 10 M events/s) into a test cluster, compared baseline (no pre‑filtering) against FluxSieve‑enabled pipelines, and measured query latency, CPU utilization, and storage growth across a variety of query types (point lookups, time‑range scans, aggregations).

Results & Findings

Key takeaways:

• Filtering at ingest cuts the data volume that the analytical engine must scan, turning expensive “filter‑then‑aggregate” queries into cheap scans over a pre‑pruned dataset.
• The rule engine scales linearly with the number of concurrent patterns, thanks to the lock‑free design.
• Integration with both a distributed OLAP system (Pinot) and an embedded DB (DuckDB) proves the approach is platform‑agnostic.

Practical Implications

• Faster dashboards & alerts – Teams can query recent logs or metrics in milliseconds, enabling near‑real‑time alerting without over‑provisioning query clusters.
• Cost savings – Reduced CPU and storage footprints translate directly into lower cloud bills, especially for SaaS observability providers handling petabytes of data.
• Simplified architecture – Developers no longer need to maintain a separate stream‑processing stack (e.g., Flink, Kafka Streams) just for pre‑filtering; the logic lives in the ingestion service itself.
• Dynamic rule updates – Security or compliance teams can push new detection patterns instantly, and the system will start applying them without downtime.
• Portability – Because FluxSieve is a thin library that can be dropped into any ingestion pipeline (Kafka Connect, Fluent Bit, custom collectors), existing observability stacks can adopt it with minimal refactoring.

Limitations & Future Work

• Rule complexity – The current DFA matcher excels at simple field predicates and regexes; more expressive SQL‑like filters (joins, sub‑queries) are not supported at ingest time.
• Stateful enrichment – Enrichments that require external lookups (e.g., service‑mesh topology) still need a separate async step, which could re‑introduce latency.
• Fault tolerance – While the authors discuss graceful rule updates, they do not fully explore recovery semantics when the ingestion node crashes mid‑batch.
• Future directions include extending the matcher to support richer predicate languages, integrating with external key‑value stores for stateful enrichment, and evaluating the approach on truly serverless ingestion environments (e.g., AWS Lambda).

FluxSieve shows that a modest amount of smart work at the data‑ingestion edge can unlock massive performance gains for cloud observability. For developers building or operating large‑scale monitoring platforms, the paper offers a practical blueprint to cut query latency, reduce infrastructure spend, and simplify the overall data pipeline.

Authors

• Adriano Vogel
• Sören Henning
• Otmar Ertl

Paper Information

• arXiv ID: 2603.04937v1
• Categories: cs.DB, cs.DC, cs.PF
• Published: March 5, 2026
• PDF: Download PDF